Introduction

Wine tasting has been shown to elicit emotions in tasters. This raises the question of what factors influence this link between emotions and tasting. The first field of research was interested in the weight of extrinsic factors related to wine on tasters' emotions. Various studies have shown that the context of the tasting (Danner et al., 2016) and the information provided to tasters, such as the level of description on the back label of wine bottles (Danner et al., 2017), affect tasters' emotional responses. Interindividual differences also influence the emotional responses to wine (Prescott, 2017). For example, in their study, Mora et al. (2018) found an impact of age and gender on the declarative emotional response during wine tasting. Mora et al. (2019) also observed an association between consumer personality and the emotions induced by wines. For instance, consumers who scored higher on neuroticism generally scored higher on negative emotions, while those who scored higher on extraversion, agreeableness and conscientiousness generally scored higher on positive emotions. Danner et al. (2020) also found that the level of consumers' involvement with wine (defined, among other things, by their knowledge of wine) and the importance of wine in their lifestyle impacted their emotional response to wine. In this study, consumers were sorted between ‘Wine Enthusiasts', ‘Aspirants' and ‘No Frills'. The more involved and higher-level connoisseur the consumer was, the higher the intensity positive emotions were felt. ‘Wine Enthusiasts' also reported stronger positive emotions when tasting higher quality and complex wines.

Finally, there is a lot of evidence that multisensory stimuli such as food and wine can induce strong emotional responses (Danner et al., 2014; Desmet and Schifferstein, 2008; van Bommel et al., 2019). Moreover, various studies in neuroscience and psychophysics have shown a strong relationship between chemical senses (especially olfaction) and emotion (Yeshurun and Sobel, 2010). In the case of wines, Danner et al. (2016) have found a link between organoleptic quality and felt emotions. In their study, Jiang et al. (2017) highlighted differences in the emotional responses according to the organoleptic profile of wines. Finally, Coste et al. (2018) explored the emotional reactions induced by the sensory properties of red wines to see how these reactions can be used to describe and evaluate wines. In their study, conventional descriptive sensory analysis, where inexperienced consumers were asked to rate different organoleptic descriptors, could not differentiate two styles of red wines from different climates. On the contrary, the analysis using emotional attributes demonstrated that these two styles of wine could be easily distinguished. The results presented in this work demonstrate that emotional responses elicited by wine could be used to differentiate wine styles. The same results were obtained by Souza-Coutinho et al. (2020) in a study on white wines.

However, these different studies are based on self-report. Only the subjective part of emotions is captured. For Scherer (2001), emotion is defined as “a set of episodic variations in several components of the organism in response to events evaluated as important for the organism” (pp. 92–120). Appraisal theories postulate that emotions are elicited by the evaluation of a stimulus by the organism. Different characteristics of the stimulus are assessed, such as novelty, pleasantness, predictability, importance for goals, cause of the stimulus, manageability of the consequences of the event and compatibility with social or personal norms (Ellsworth and Scherer, 2003; Coppin and Sander, 2016). Scherer (2001) identifies five components of the emotion: cognitive evaluation of stimuli, physiological, motor expression, motivational (action tendencies) and finally, a subjective feeling component, which reflects the emotional experience, and which would be the reflection of the changes occurring in all the components. Scherer (2001) emphasises dynamic interrelationships between the five components of emotion. Appraisal theories of emotion seem to provide the best framework for studies on wine tasting. Indeed, in their study, Coppin et al. (2021) found that many relevant factors, such as self-reported expertise, tasting conditions and sensitivity to key information about wine, impact the subjective emotional response to wines. These results correspond with the theoretical context of appraisal, according to which emotions provoked by a stimulus arise after evaluating certain criteria of interest. In the study of sensory stimuli other than wines, such as odours (Delplanque et al., 2009), the appraisal theories of emotions have proven to be effective. Danner et al. (2017) also show that consistency with expectations about a wine influences the intensity of the emotions felt during tasting. Once again, these results are consistent with appraisal theories where the predictability of a stimulus is one of the parameters assessed to trigger the emotion.

Thus, according to appraisal theories, to complete the characterisation of the emotions felt during tasting, it is necessary to carry out complementary measurements to the simple self-report of the consumer's subjective emotions. Two kinds of emotional responses can be distinguished and measured. As in the studies cited above, the first one is a subjective, explicit and evaluative response, the subjective feelings. It can be through verbal expression (through emotional lexicon) and through the auto-declarative evaluation of valence (pleasure/displeasure) and arousal (weak/strong). Ferrarini et al. (2010) developed a list of 16 emotional terms appropriate to Italian culture to describe emotions elicited by wine consumption, divided into two categories: high/low arousal and high/low pleasantness. In this study, most of the terms selected to describe emotions in wine tasting were emotional terms associated with pleasure. Silva et al. (2016) also showed an association between wine and positive low-arousal emotional responses, with emotional terms such as calm and loving in a population of Dutch and Portuguese consumers. Monseau and Deneulin (2014) developed a lexicon of 34 emotional terms relevant to wine tasting in Swiss culture. This study also showed that wine is more associated with positive than negative emotions and that emotions are strongly linked to the quality of wine. In their study, Danner et al. (2016), by adapting an emotional lexicon for Australian wine consumers, also showed that high-quality wines elicited more intense positive emotions compared to wines of lower quality.

The second kind of emotional response is an automatic, implicit and uncontrolled response. This response has a motor component that passes through facial expressions or changes in the Autonomic Nervous System (ANS). Bensafi et al. (2002) showed that ANS activity, such as heart rate and skin conductance, presented variations among dimensions of pleasantness and arousal. A strong correlation exists between the pleasantness factor of emotional responses and heart rate variation. In fact, an increase in heart rate variation is observed in a rejection context, for example, in response to unpleasant odours (Bensafi, 2002; Bensafi et al., 2002). Electrodermal activity has long been used to indicate arousal in psychophysiological studies. Bensafi et al. (2002) observed a positive correlation between arousal and skin conductance level (SCL) variation for odours. For more complex stimuli such as juices, Danner et al. (2014) showed that tasting different juices elicited different ANS responses for skin conductance level and pulse volume amplitude and that there is a differentiation between liked and disliked samples for the intensity of different facial expressions such as “disgusted” or “neutral”. They also suggest that ANS and facial expression provide different information than that of self-reported appreciation of the product. For food products, physiological measurements and motor measurements could provide information on food preferences that may not be provided by other more explicit tests (De Wijk et al., 2012; De Wijk and Noldus, 2021; Spinelli and Jaeger, 2019). To our knowledge, this automatic response has never been studied in the wine field and seems a relevant perspective to characterise the specificity of emotions elicited by wine organoleptic properties (Niimi et al., 2019).

This study aimed to determine whether the automatic component of emotion has a measurable output (motor and physiological) during wine tasting. The first approach was to assess the degree of discrimination of wines using these measures. The second was the verification of correlates recognised in the literature between subjective feelings and motor or physiological components in a wine-tasting situation.

Materials and methods

4. Procedure

The experiment took place during individual one-time sessions in the morning (10 AM) for a duration of 2 hours. At the rate of one session per day, the 65 sessions took place over 15 weeks. The room in which the experiment took place was equipped with a non-tinted mirror overlooking another room in which the experimenter was present so that the presence of the experimenter would not disturb the participants' concentration and emotional response.

After an explanation of the experiment and the signature of the participant's consent form, the participant was equipped with the devices necessary for the acquisition of physiological measurements. The participant was then placed in front of a screen, on which the various instructions relating to the experiment were displayed, including a voice recording broadcast via an audio speaker. All instructions were carried out using the PsychoPy v2021.2.3 software.

A phase of familiarisation with the equipment and procedure was carried out using spring water. The participants were informed that they would taste different red Bordeaux wines. During the evaluation, the samples were evaluated in monadic sequential. On average, twenty-five millilitres of each wine were presented in a transparent glass (ISO 3591, 1977). The samples were presented anonymously (random three-digit coding) and served randomly for each participant, following an optimal randomised plan to avoid possible alcohol-related biases.

For each sample, the following instruction was given to the participant (in French):

“At the sound signal, take glass number XXX and evaluate its visual, olfactory and gustatory properties. When you have the wine in your mouth, stay a few moments in front of the screen. Spit, then stand facing the screen again for a few moments before raising your hand.” (Instruction 1, see Figure 1).

For each participant, the first sensory contact with the wine sample (whether visual, olfactory or gustatory) represented the beginning of the overall tasting and the beginning of the measurements. The end corresponded to the time when the wine was spat out. The tasting time was left open and therefore depended on each participant.

When the participant raised their hand after the tasting, the following instructions were given: “Fill in the evaluation sheet and raise your hand when you have finished” (Instruction 2, see Figure 1).

The time to complete the declarative part was open. When the participant raised their hand after filling it in, they moved on to the next sample of wine.

Figure 1. Extract of the experimental procedure for the evaluation of one wine.

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The instructions (displayed on the screen and broadcast in French through a speaker) were as follows: Instruction 1: At the sound signal, take glass number XXX and evaluate its visual, olfactory, and gustatory properties. When you have the wine in your mouth, stay a few moments in front of the screen. Spit, then stand facing the screen again for a few moments before raising your hand. Instruction 2: Fill in the evaluation sheet and raise your hand when you have finished.

Results

1. Differences between wines

1.1. Valence score

The minimum valence score for all wines was 1, and the maximum was 9. The entire scale was therefore used, but a participant-dependent variability in the use of the scale was observed. Therefore, to compensate for these differences between participants in the use of the scale, the valence scores have been standardised (centred-reduced).

An ANOVA test with Tukey post-hoc tests showed that valence scores significantly differed between Wine 1 and Wine 4 and 7 (F(6,441) = 3.36; p-value = 0.003) (Figure 2A). A continuum in the valence score attributed to the wines is observed.

1.2. Arousal score

The minimum arousal score for all wines was 1, and the maximum was 9. The entire scale was therefore used, but a participant-dependent variability in the use of the scale was observed. Therefore, to compensate for these differences between participants in the use of the scale, the arousal scores have been centred-reduced.

An ANOVA test indicated no significant differences between the wines for the arousal scores (F(6,448) = 1.6; p-value > 0.05) (Figure 2B).

The low discrimination observed between the wines for the valence and arousal scores can be explained by a strong inter-individual variation in the scores.

Figure 2. Boxplots of the valence score distribution (2A) and the arousal score distribution (2B) for the seven wines.

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The white cross represents the mean score for each wine; the extremities represent the minimum and the maximum values for each wine. Values marked with different letters are significantly different (Tukey post hoc p-value ≤ 0.05).

1.3. Emotional lexicon

The citation frequency of each emotional lexicon term was calculated (Figure 3).

Five terms had a citation frequency below 5 % (“Euphoric”, “Passionate”, “Audacious”, “Sensual” and “Contemptuous”), 8 terms had a citation frequency between 5 and 10 %, and 12 terms above 10 %. The maximum citation frequency was observed for the term “Curious” (27.7 %).

A Cochran test for comparison of citation frequencies per wine revealed that the terms “Contemptuous”, “Elegant” and “Surprised” were used differently to describe the wines (Cochran test, p-values of 0.029; 0.007 and 0.0002, respectively). Note that these terms were, respectively, associated with 4.2 %, 6.8 % and 22.8 % citation frequencies on all wines. Only the term “Surprised” had a citation frequency superior to 10 %. Sheskin's pairwise comparison tests showed only significant differences in occurrence for the terms “Contemptuous” and “Surprised”. There was no difference between the pairs of wines for the term “Elegant”, although its citation frequency was higher for Wines 5 and 6. The term “Contemptuous” was significantly more common for Wine 1 than for Wine 5 (p-value = 0.029), and the term “Surprised” was significantly more common for Wine 1 than for Wines 2, 4, 5 and 7 (p-value < 0.001).

Figure 3. Percentage of participants who used each emotional term (n = 65). The lexicon was presented to the participants in French.

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1.4. Physiological measures

Recording artefacts required the exclusion of 16 of the 65 participants from heart rate measurements. Heart rate variation rates ranged from –6.7 to 29. A Kruskal–Wallis test between wines revealed that heart rate measurements did not significantly differ between wines (Q = 6.33, ddl = 6, p-value = 0.387).

For the measurement of electrodermal activity, 14 % of participants were excluded because they did not respond to this measurement. This corresponds to data in the literature, according to which 10 % of participants may be “non-responders” (Biopac Systems Inc). In other words, participants fail to elicit SCR oriented to a stimulus. In addition, recording artefacts forced the exclusion of two participants from all electrodermal analyses and four additional participants for skin conductance level measurements.

A Friedman test indicated a significant difference in the amplitude of electrodermal responses between wines (Friedman test, F = 13.22(6), p-value = 0.04), with Wine 1 having the highest score and Wine 6 having the lowest score. However, Nemenyi's post-hoc tests did not reveal any significantly different pairs of wines. A χ² test revealed that the number of electrodermal responses differed between wines χ² = 14.78, ddl = 6, p-value = 0.011), with a maximum for wine 1 and a minimum for wine 6. However, the Marascuilo procedure fails to identify pairs of wines that are significantly different (p-value > 0.05).

Finally, a Friedman test indicated no significant differences in skin conductance level variation between wines (F = 4.78, p-value = 0.573).

1.5. Motor measures

Recording artefacts required the exclusion of 15 of the 65 participants from the motor measures. Friedman tests indicated no significant differences in the different measures of the motor component between the different wines (p-value > 0.05).

2. Relationship between subjective feelings and physiological and motor components of emotions

2.1. Relationship between subjective feelings and physiological component

2.1.1. Heart rate variation ratio and valence score

For each participant, and to take into account possible interindividual variation in emotional assessment, the valence scores given to the different wines were transformed into ranks. For each subject, each wine was assigned a rank ranging from 1 to 7. Finally, three groups were formed:

- The wines classified from 1 to 3 were categorised in the “negative valence” group.

- The wines classified in rank 4 were excluded: wine not providing neither pleasure nor displeasure. These wines were excluded from the analysis to avoid having neutral wines and include only wines for which an opinion towards one extreme or the other was made.

- The wines classified from 5 to 7 were categorised in the “positive valence” group.

For both groups of wine, an increase in the heart rate variation ratio after tasting was observed. The increase observed with wines in the “positive valence” group was barely greater than the increase observed with water (baseline with a neutral stimulus). Moreover, a significant difference was observed between the groups (Mann–Whitney test, U = 10762, p-value = 0.008). The heart rate variation ratio was significantly higher for the wines providing unpleasant emotions, in other words, in the context of rejection (Figure 4).

The same approach was repeated with the arousal note, and no significant difference was observed (U = 8645, p-value > 0.05).

Figure 4. Mean variation (± 5 % confidence interval) of heart rate variation ratio for wines in “Negative valence” (n = 206) and “Positive valence” groups (n = 213).

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The black line represents baseline measures with water (Mann–Whitney test). ** p-value < 0.01

2.1.2. Electrodermal activity and arousal score

Figure 5 below represents the relationship between the weighted mean number of sensory event-related SCRs (across all participants) and the average arousal score for each wine. A positive correlation was observed between the arousal score and the total number of sensory event-related SCRs by wine (linear regression, R² = 0.87). Although arousal scores were not significantly different between wines, the wine showing the maximum number of electrodermal responses was the one associated with the maximum average arousal score, wine 1. The same observation was made for the minimum with wine 6.

Figure 5. Weighted mean number of sensory event-related electrodermal responses for all participants for each wine according to the average centred-reduced arousal score.

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Linear regression (R² = 0.87).

To account for possible interindividual variation in arousal, the same approach as described for valence above was used to study the relationship between SCR and SCL measures. For each participant, the arousal scores given to the different wines were transformed into ranks. For each subject, each wine was assigned a rank ranging from 1 to 7. Finally, three groups were formed:

- The wines classified from 1 to 3 were categorised into the “low arousal” group.

- The wines classified in rank 4 were excluded: wine not providing either low or high arousal. These wines were excluded from the analysis to avoid having neutral wines and only wines for which an opinion towards one extreme or the other was made.

- The wines classified from 5 to 7 were categorised into the “high arousal” group.

Mann–Whitney tests were carried out between the “low arousal” group and the “high arousal” group to see if the wines of these groups would induce different variations of SCL and different amplitudes of SCRs.

The average SCL level decreased for the two wine groups after tasting, but this decrease was greater for the “low arousal” group, although not significant (U = 13578.5, p-value = 0.101) (Figure 6A). A significantly greater amplitude of the sensory event-related SCRs for the wines in the “high arousal” group was observed compared to the “low arousal” wines (U = 12027.5, p-value = 0.05) (Figure 6B).

The same approach was repeated with the valence note, and no significant differences were observed for the mean variation of SCL (U = 13362, p-value = 0.2) or for the mean amplitude of normalised sensory event-related SCRs (U = 13270, p-value = 0.991).

Figure 6. Mean variation of SCL (± 5 % confidence interval) for wines in “low arousal” and “high arousal” groups (A), mean amplitude (± 5 % confidence interval) of the normalised sensory event-related SCRs for wines of “low arousal” group and “high arousal” group (B).

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The black line represents the baseline measures with water. Mann–Whitney tests. * p-value < 0.05.

2.2. Relationship between subjective feelings and motor components

The same rank data transformation described above was used on the valence score. Mann–Whitney tests were used to test for differences in the number of action units between the “negative valence” group and the “positive valence” group.

The total number of action units activated during smelling, tasting, or after spitting the wine didn’t show any significant differences between the “negative valence” and “positive valence” groups (Mann–Whitney tests, p-value > 0.05).

The total maximum activation intensity of action units during smelling or tasting the wine didn’t show any significant differences between the “negative valence” and “positive valence” groups (Mann–Whitney tests, p-value > 0.05).

A marginally significant difference was observed between the “negative valence” and “positive valence” groups for the total maximum activation intensity of action units after spitting the wine, in other words, when the tasting was complete (U = 14045, p-value = 0.086). The “negative valence” group displayed higher total maximum activation intensity than the “positive valence” group. This value is higher for the “negative valence” group than for the “positive valence” group. The mean value of the “positive valence” group is negative (–0.044) (the mean value of this measurement after spitting water was greater).

The same approach was repeated with the arousal note, for which no significant differences were observed (Mann–Whitney tests, p-value > 0.05).

Discussion

The main purpose of this study was to determine if wine tasting induces a concomitant activation of the different components of emotion (subjective feelings, physiological and motor components).

This study revealed that only the subjective component (valence and emotional lexicon) and electrodermal measurements differed between wines. Wine 1 with a defect seemed to be the common denominator of these discriminating measures. No differences were observed for heart rate or motor measures. Pichon et al. (2015) highlighted the difficulty of discriminating different products of the same family (in this case, fragrances) and of a close range of pleasantness with measurements such as physiological measurements (heart rate and SCR). These authors assume that, in the case of a range of closely related products, variations in these different physiological parameters may occur but are too subtle to be measured with easy-to-set-up measurements such as the ones used in this study. Furthermore, according to De Wijk and Noldus (2021), automatic measurements reflect the experience of consumption as a whole, taking into account the food product itself but also the context and the social interactions surrounding the consumption. This last assertion may explain the low discrimination capacity of wines by action unit measurement. De Wijk and Noldus (2021) suggest that, with well-liked food, these implicit measurements would present advantages over explicit measurements in a real-life situation outside of a controlled laboratory setting. However, the many signal disturbances related to the movements of the participants during the taking of these measurements (particularly the electrocardiogram) seem to make it complex to take these measurements in a less controlled context. Beyts et al. (2017) obtained similar results for beer and suggested that if their samples were well discriminated by declarative but not physiological measures, this could be due to the fact that the beer aromas might be too similar to induce differences at the physiological level. In the present study, to assess the association between motor components of emotion and valence, motor expression was studied through facial expressions. In previous work with juice samples, Danner et al. (2014) showed that the intensity of different facial expressions allowed them to differentiate liked samples from disliked ones. Our results displayed a marginally significant difference between wines providing pleasant emotions and wine providing unpleasant emotions for the total maximum activation intensity of action units after spitting the wine. This value is higher for wines providing unpleasant emotions than for wines providing pleasant emotions. Although these results are consistent with those of Danner et al. (2014), this measurement provides only moderate discrimination. The first limitation of measuring facial expression in a wine-tasting context can be a logistical limitation. For instance, the fact that participants had the glass in front of their faces, especially when smelling the wine, resulted in several biases, and the software could not manage to acquire measurements. Furthermore, Mahieu et al. (2019) evaluated the measurability of emotions through facial expressions for food products. Their study showed that facial expression measurements are more suitable for stimuli such as video advertisements, in other words, standardised stimuli designed specifically to elicit emotions. According to the authors, facial expressions seem to not allow for the discrimination of food products of the same type. The subjective feelings measurement (emotional lexicon), which is much less expensive and laborious to set up, seems to be sufficient within the framework of the study of emotions in wine tasting. Physiological and motor measurements could, however, prove to be a useful tool in the context of certain studies, for example, on people who are unable to identify and describe emotions felt.

Finally, although these measures are more or less discriminating, the correlates found between subjective and physiological data validate the approach and the measurements carried out. Indeed, correlates reported in the literature for other sensory stimuli are also observed for wines.

Conclusion

The results presented in this study, therefore, show that there is a measurable physiological and motor output in tasting. Out of the five components of emotion in the appraisal theories identified by Scherer (2001), our study validates three: the physiological component, the motor component, and subjective feelings. However, research remains to be done to validate the remaining two (cognitive evaluation of stimuli and motivational (action tendencies) in the context of wine tasting, in accordance with the appraisal theories. The relationships established between these components, as well as their concomitant activation, validate the reality of the emotion felt during tasting. That is, the link between these different emotional components implies that participants do not declare feeling a specific emotion because they were asked to choose among different emotional terms but because they really feel them. Especially since a link with automatic physiological measurements was found. This same link, conversely, also makes it possible to conclude that the observed physiological variations are indeed to be attributed to an emotional response.

Finally, wine is indeed a purveyor of emotions, an emotional object. The wine sector has a real interest in appropriating this measure to use the emotional impact of the organoleptic characteristics of wines as a lever of characterisation, differentiation and valuation. The innovative point of this study is highlighting the inter-relationship between the different emotional components. This offers perspectives for better understanding the intertwining relationship between emotions and wine and opens scientific doors to the oenological sector by providing a promising basis. A recent study by Caissie et al. (2023) has shown that emotions can be one of the dimensions of the organoleptic quality of wines. It, therefore, seems important to be able to find the tools to characterise them.

Acknowledgements

The authors would like to acknowledge the support received from La Région Nouvelle-Aquitaine and Le Conseil Interprofessionnel du Vin de Bordeaux.

The authors would like to sincerely thank Emilie Cousin and Pascal Hot of the Laboratoire de Psychologie et de NeuroCognition (LPNC - CNRS UMR 5105 - Grenoble, France) for their advice.

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